Understanding the value of social media monitoring and visualisation tools for government crowdsourcing

Transcription

1 Understanding the value of social media monitoring and visualisation tools for government crowdsourcing Study report for NEMODE pilot project (New Economic Models in the Digital Economy) June 2015

2 Preface and acknowledgements Social media monitoring and visualisation tools can be a source of ideas to improve inclusivity in policy development beyond traditional influencers. The aim of this project is to develop both practical insights and an academic case study of how social media data, particularly from Twitter, are shaping new modes of governance in the digital economy. The report describes a pilot application of social media analysis and visualisation in the area of food and farming regulation. The study involved two main tasks: (1) an online data collection and analysis exercise to map farming and agricultural networks on Twitter and (2) a series of interviews and workshops to examine key challenges and information requirements related to the use of social media data. The research was carried out with the support of a partner organisation, the Better Regulation Team of the Department for Environment, Food and Rural Affairs (DEFRA). The online data collection exercise was based on Chorus (chorusanalytics.co.uk), which is a social media analytics tool designed to support social science research. Drawing from this case study, the report discusses key challenges for government departments and other public sector organisations in the process of adopting similar tools. The project team gratefully acknowledges interview and workshop participants from DEFRA. We are particularly thankful to Jenny Buckland, Dawn Woodward, Lorraine Horwood and Michelle Charlton for providing all the necessary support for the project to take place. We further acknowledge the kind support of Edward Morgan, Charles Tassell, Jez Fredenburgh and Simon Haley, farming entrepreneurs and founders of AgriChatUK; a national Twitter conversation about farming that has been the starting point of our analysis. We are additionally thankful to Simon Haley for sharing earlier academic work on digital agricultural networks (Haley, 2013). 2

3 Project team Dr Panos Panagiotopoulos Investigators Queen Mary University of London Prof Frances Bowen Queen Mary University of London Interviews with DEFRA and workshop organisation Dr Chris Taylor Verascio Ltd Dr Philip Brooker University of Bath Chorus Analytics team Dr Timothy Cribbin Brunel University London Prof Julie Barnett University of Bath For comments related to the project or the content of this report, please contact Panos Panagiotopoulos at 3

4 Contents Preface and acknowledgements Project team Motivation: the value of online information sources in policy 5 2. Background: social media monitoring tools Features of social media management and monitoring tools Chorus Analytics: a research- based social media monitoring platform Study context and aims The partner organisation Exploring farming and agricultural networks on Twitter Workshops and interviews with key informants Findings: farming and agricultural networks on Twitter #AgriChatUK: a national conversation about farming National Farmers Union (@NFUTweets) followers Findings from interviews and workshops Sources and forms of social media input Conceptualisations of the social media audience Summary of workshop findings Conclusion and recommendations 31 References 33 4

5 1. Motivation: the value of online information sources in policy Online information sources are becoming increasingly relevant to the work of government due to several important developments like: Advancements in the areas of social media analytics, crowdsourcing and big data research both in terms of technical capabilities and general awareness about the importance of data in business and society (e.g. Schintler & Kulkarni, 2014; Lodge & Wegrich, 2014; Prpić et al., 2015). The open government and transparency agenda where the role of data and digital interactions have been particularly highlighted (e.g. Obama, 2009; Bertot et al., 2010; 2012). Budget reductions that stimulate policy makers to seek alternative, more cost effective and usually less direct means of developing policy and regulating behaviour (UK Government, 2015). The widespread adoption of social media for information sharing and networking amongst different groups of the public arguably more than ever at both a social and professional level in the United Kingdom (Dutton et al., 2013; Ofcom, 2014). New approaches to policy and regulation using online information sources can bring potentially radical changes in how government regulators source feedback from the public. Information flows between governments and the public that used to be private and unidirectional, might now take place in open spaces possibly not initiated or controlled by government. Inevitably, indirect approaches based on social media data require new capabilities such as understanding how public conversations evolve, what is seen as socially acceptable, and who the key stakeholders are (Castelló et al., 2013). In theory, social media have become information sources where content produced can inform evidence- based policy development. However, in practice, government departments are still focused on disseminating information rather than understanding social conversations (Mergel, 2013a; 2013b). When they do so, they are likely to use commercial social media analytics platforms that are based on content metrics developed to assist decisions in a commercial communications context (Mergel, 2012; Panagiotopoulos et al., 2015), (e.g. collecting reactions to content in the form of likes or retweets ). This approach might support an overview of emerging trends but does not align well with government's broader mission to influence, engage and build permanent relationships with user networks. 5

6 Therefore, there is need to look more closely at how social media monitoring tools could more effectively support policy and regulatory activities and decisions. Specifically, the project focuses on exploring the technical and organisational requirements of social media monitoring tools with the aims of: Developing proactive understanding of information networks that can inform evidence- based policy making (e.g. across different stages and activities of the policy making lifecycle). Improving inclusivity in policy development beyond traditional influencers such as organised lobbyists (e.g. how groups of the public use social media). The project s main activities evolved around a pilot study in the area of food and farming regulation with the support of a partner organisation, the Better Regulation Team of the Department for Environment, Food and Rural Affairs (DEFRA). The study involved two main tasks: (1) an online data collection and analysis exercise to map farming and agricultural networks on Twitter and (2) a series of interviews and workshops to examine key challenges and information requirements related to the use of social media data. Data from Twitter were sourced and analysed using Chorus Analytics, which is a research- based social media analysis tool designed to support social science projects (chorusanalytics.co.uk). Details of the study are described in section 3 following some background on social media monitoring and visualisation tools in the next section. The findings show that there is good scope for sourcing social media content as long as it is appropriately summarised and includes useful information about the composition of digital audiences and the context in which content is produced. The more these conditions are met, the more contributions from the public can be seen as a useful resource in policy decisions. The report concludes by discussing key technical, methodological and policy challenges. 2. Background: social media monitoring tools Social media monitoring has gained attention in digital government research following a first wave of social media use by governments to inform and engage (e.g. Criado et al., 2013; Chun et al., 2010; I Mergel, 2013). Social media monitoring broadly belongs to crowdsourcing practices where a large number of individual contributions are aggregated and analysed with the aim of co- creating value (e.g. solving a problem, funding a new project) (Estelles- Arolas & Gonzalez- Ladron- de- Guevara, 2012; Chiu et al., 2014). The importance of crowdsourcing has been evident in public management with popular platforms that invite contributions from the public like Challenge.gov and the Open 6

7 Government Public Engagement Platform in the USA (Mergel & Desouza, 2013; Linders, 2012) or the UK government s Red Tape Challenge that collects feedback on regulations (Lodge & Wegrich, 2014). Social media monitoring is a more ad hoc and less institutionalised form of crowdsourcing where, compared to platforms that have an explicit crowdsourcing role, contributions from the public are sourced from open spaces where they have been posted with different original intentions. This activity resembles traditional debriefings where extracts from the printed press or television are sourced and summarised based on the relevance of their content. The main difference with social media monitoring is the vast amount of potential sources and technical complexities involved in filtering and aggregating content. This is why, from a more technical perspective, Charalabidis et al. (2014; 2012) have distinguished this approach as passive or non- moderated crowdsourcing where appropriately designed systems aggregate content to enable policy makers to overview a large amount of information. Whether labelling social media monitoring as a form of crowdsourcing or not, from a policy perspective, it is increasingly of interest that social media users provide direct feedback on policy topics, broadcast regular information about their activities or generally engage in online interactions that can be informative for the work of government. Some of these interactions take place on closed social networking pages or groups (e.g. Facebook, LinkedIn); others in open but not immediately visible spaces (e.g. Twitter conversations). This is the main reason that over the last years, new social media management and monitoring tools have become available. We review their main features next before making the case for a different approach using research- based tools like Chorus Analytics Features of social media management and monitoring tools Every social media channel has its own content recommendation and monitoring features so that users can create custom information flows, identify and filter content, connect to others and generally stay engaged. These different functionalities allow organisations and individual users to develop their presence, connect with others and share content according to their diverse aims (e.g. both social and professional) (Kietzmann et al., 2011). On some occasions, social media users might have obvious motivations to engage on particular platforms (e.g. LinkedIn for professional networking), but on others these boundaries might be blurred both in terms of content (e.g. Twitter updates) and composition of networks (e.g. Facebook friends). It is furthermore common to observe dynamic audiences on social media 7

8 that form temporarily around events or interests like TV shows (Highfield et al., 2013; Brooker et al., 2015). As a result, it is inevitable that there is a certain level of audience fragmentation and distribution of activities across platforms. For advanced social media users that have a commercial interest in user- generated content, this creates two interrelated needs: (a) managing social media interactions consistently across platforms and multiple users within the organisation and (b) filtering and monitoring content beyond basic features like Twitter and Facebook timelines. These needs have led to the development of specialised social media management and monitoring solutions. We now need to consider the features of these tools that support the work of advanced users. First, social media management tools enable the administration and oversight of multiple accounts; for example, with one click someone can start a new post and then choose which accounts to update from a menu of choices. Indicatively, the popular HootSuite (hootsuite.com) offers integration with Twitter, Facebook, Google+, Pinterest, RSS feeds, Instagram, LinkedIn, Foursquare, Wordpress and MySpace 1. Furthermore, there are usually features that support the following: Scheduling and planning content so as to maximise its reach (e.g. updating a blog or posting a tweet during specific times within a day). Central notifications of incoming interactions from other users (e.g. Twitter mentions, Facebook likes). Audience management features like list of connections, statistical information about connections, setting and meeting targets (e.g. number of Twitter followers gained or lost within a week). Options for multiple users and devices so that many people within an organisation can access social media accounts from different roles. These features are an important step towards integrated social media management and, at the next level, they are followed by features to monitor social content produced by other users. Monitoring features usually include some of the following: 1 HootSuite is one of the widely used social media monitoring tools, arguably because it offers a combination of basic and more advanced features that suit the needs of many users without significant investment. Other tools offer more advanced monitoring and reporting features. Some examples in random order are: Topsy, Radian6, SproutSocial, Social360, Brandwatch, Engagor, Trackur, Sysomos, Nielsen BuzzMetrics, Buzzient, Social Radar, Social Mention, Klout, Seesmic and many others. A full review of commercial monitoring tools is outside the scope of this report. 8

9 Ad hoc or real- time monitoring of trends (e.g. what is trending in the UK today ) or specific searches based on keywords (e.g. what are people saying about our new product ). In its more basic form, this type of monitoring is shown in a set column displaying tweets that contain a specific keyword or hashtag. Figures 1 shows an example from TweetDeck, a monitoring dashboard application acquired by Twitter. Figure 1: TweetDeck screenshot, an example of social media monitoring dashboards dashboard Scheduled and periodic monitoring that includes features like traffic sources, key performance indicators, summary statistics about engagement with content (e.g. number of retweets) and other measures of reach, influence and reputation. Users are usually able to filter all relevant metadata included in social media content (e.g. location of tweets, LinkedIn company information). Sentiment analysis is a common feature that classifies tweets as positive or negative based on a predefined scale. In large volumes of content and over time, sentiment analysis can indicate how public opinion changes about a topic (e.g. social crisis or support for a public figure) (Thelwall et al., 2010; Bae & Lee, 2012; Thelwall et al., 2011). Some form of network analysis mainly in terms of identifying top contributors within a domain or discussion. Top contributors or influencers are usually defined based on the size of their networks (e.g. followers ) or reach of their content. Reporting and exporting options, dashboards and other visualisations that help users navigate through the above features and filter information. 9

10 Overall these tools offer some powerful technical features that can support an organisation s ability to promote content, monitor and engage. One of their common grounds however is that they are based on measures of popularity metrics or analytics that are tailored for domains like marketing where there needs to be a clear return on investment (e.g. Hoffman & Fodor, 2010; Fan & Gordon, 2014; Sterne, 2010). This is why the designed interfaces and underlying functions are usually optimised for applications like brand engagement, reputation management where counting social impressions ( buzz ) is important. More generally, these tools can provide adequate support when there is a clear set of keywords to be monitored or during specific events like emergencies, international sport competitions or campaigns. Given their focus on content discovery and popularity, these tools can be useful but lack specific consideration of the needs of policy makers. For example, to use content from digital audiences as a resource in policy making, there need to be some features that facilitate an understanding of how public conversations evolve, how networks form between stakeholders in policy areas and how social media users comment on the work of government (but not necessarily like or share this work). Briefly, it could be argued that these requirements link with some of the principles of social science research instead of marketing applications. To this end, we introduce Chorus Analytics a research- based monitoring platform that was used as a pilot tool in this project Chorus Analytics: a research- based social media monitoring platform Chorus is a research- based analytics suite that is able to capture and visualise data from Twitter. Chorus provides two ways of capturing data: by keyword queries (e.g. #AgriChatUK) or by user timelines (capturing the Twitter timelines of selected users, From here, these data can be visualised in two ways. The first set of visualisations is timeline- based, and designed to show fluctuations in various metrics across time intervals. These metrics include: tweet volume; proportion of tweets within an interval which contain a URL; positive and negative sentiment 2 as well as the novelty and homogeneity of talk across and within intervals. This view allows Chorus users to explore the unfolding narratives contained within Twitter conversations, allowing for an event- based analysis of the data at hand which can be attuned to the changing and shifting nature of tweeters' talk 2 Chorus implements the SentiStrength algorithm, which estimates the strength of positive and negative terms in short texts with up to human- level accuracy: see sentistrength.wlv.ac.uk for further details. 10

11 and interactions. The sample view shown in figure 2 has been used to undertake an analysis of public perception of risk around the 2011 UK e. coli food scare, concentrating on how talk around the issue shifted from the sharing of news article URLs to opinions and how tweeters' concerns developed across different food products 3. Figure 2: a sample Chorus dashboard of timeline data from the 2011 E.coli incident The second mode of visualisation offered by Chorus is the Cluster Explorer view, which relies on topical cluster maps to display groupings of commonly co- occurring words within the whole dataset. Chorus' cluster map algorithm draws on a generated word index, which computes a strength value for all co- occurrences between terms in a given dataset. Words that are mentioned together in tweets more frequently are given a higher co- occurrence value to reflect the strength of their association. The Cluster Explorer view uses this index to draw a series of cluster maps, where strongly associated terms, tweets and intervals are plotted closer together. In this way, Chorus visualises an entire dataset as a connected collection of clusters of strongly associated words. The closer different words appear in the cluster map, the more frequency they co- occur in tweets. The size of each node, representing a keyword, is proportional to the frequency of this keyword mentioned in the 3 For more details about this case study based on a slightly expanded dataset see (Gaspar et al., 2014). 11

12 dataset. The Chorus Cluster Explorer can be used to develop topic- based analyses of Twitter data as a collection of distinct topics and sub- topics and the tracing of connections between them. This view has been used, for example, to inform research work into sufferer experiences of living with cystic fibrosis; a chronic health condition affecting the lungs (Brooker et al., 2014). Here, as shown in figure 3, the Cluster Explorer was used to identify distinct topics of conversation amongst tweeters with cystic fibrosis, and uncovered conversations around the topics of double lung transplants, symptom management, charity events in support of cystic fibrosis research and so on. Figure 3: sample Chorus cluster explorer map to summarise data related to cystic fibrosis Both these views are complemented with various sortable statistics at the dataset and interval level to allow users to relate visual insights back to individual tweets in the Tweet Reader panel (e.g. frequency of keywords, hashtags, users and links). The visualisations and statistical information are designed to work interactively to help users dig into 'big' data in a qualitative fashion. Overall, the main difference between Chorus and commercial social media monitoring tools is the depth of functionality that can summarise Twitter data with several timeline and theme- mapping options. 12

13 3. Study context and aims Between November 2014 and March 2015, we worked with the partner organisation to organise the pilot monitoring and visualisation exercise that could act as a springboard to discuss wider issues around the value of social media data. After an initial exploration of different policy themes and areas, we decided to focus on doing this in relation to farming and agriculture. The results of this exercise were then used as input for further discussions, research interviews and the project s final workshop. Ethical approval for the study was provided by Queen Mary University of London (code QMERC1334) The partner organisation DEFRA is one of the largest government departments in the UK with remit in policy and regulation related to environmental protection, food production and standards, agriculture, fisheries and rural communities. DEFRA s objectives and priorities include the improvement of technical infrastructure in rural areas, increasing exports and competitiveness in the food chain, simplifying farming regulation and improving water quality (DEFRA, 2010). The department employs over 10,000 staff working across 36 agencies and public bodies in England with devolved administrations in Wales, Scotland and Northern Ireland. There is also extensive cooperation with European Union authorities for environmental policies, including the high profile Common Agricultural Policy that involves a system of agricultural subsidies and programmes for farming and rural development. DEFRA makes an interesting case for the pilot application of data analysis and visualisation tools due to the organisation s focus on: Developing capabilities for evidence- based policy making collecting evidence from as many sources as possible involving policy consultations, social science research and public understanding studies. Implementing initiatives that aim at positive behavioural change (e.g. sustainable consumption, energy labelling, reducing food waste) and Carrying out wide stakeholder management activities involving professional associations, academic research teams and other external experts. This approach to policy making is summarised in the form of the Policy Cycle shown in Figure 4. Compared to more general models, it places emphasis on issue definition and situation understanding. This is necessary for the work of DEFRA due to the high complexity, economic impact and technical nature of environmental issues as well as the wide variety of stakeholders usually involved. 13

14 It is also important to note that DEFRA s engagement and dissemination activities also take place around the department s social media accounts. Digital presence has been organised on Facebook, LinkedIn, Google+, Storify and Flickr as well as 12 different Twitter accounts, which support diverse policy areas and Figure 4: DEFRA Policy Cycle communication needs. For example, Twitter accounts include advice lines for farmers and fisheries, rural news, updates on the availability of official statistics, a specialised account on Smarter Guidance and Data and news feeds on air or water quality. The main Twitter has over 70K followers while the more specialised accounts might have from fewer than 1K followers to over 5K. Some of these feeds are generated automated while most are managed individually by policy teams following internal guidance and training. Most of the department s 36 agencies and public bodies also manage their own social media presence on a selection of channels. Some of these accounts are clearly informational in their purpose (news feeds) while others experience varying levels of interaction with the public Exploring farming and agricultural networks on Twitter The UK government has generally shown interest to explore opportunities for the strategic promotion of social media and crowdsourcing initiatives like the Red Tape Challenge. The choice of farming and agricultural networks for the scope of this study was informed by issues of access within the partner organisations but also because it is a novel, unexplored field that represents a set of interesting challenges to policy and regulation. Agriculture is a large part of the UK economy with an estimated number of 250K farmers across the country (DEFRA Statistics, 2015). Farmers and agricultural businesses form a large, geographically dispersed community that is subject to a variety of regulatory requirements from complicated technical issues about environmental protection to European laws and issues mandated by the Common Agricultural Policy. A Farming Regulation Task Force within DEFRA has been in place since 2012 to review farming policy and regulations. Proposed measures to simplify regulations include aspects 14

15 like facility inspections, storing and sharing information, animal movements, environmental requirements, payments and other issues related to the Common Agricultural Policy. There is also a commitment to help farmers move online, which includes measures to improve broadband connectivity in rural areas and introduce new digital services for payments, reporting and monitoring (e.g. Online Sheep and Goat Inventory) (DEFRA, 2014). Over the past years, there have been positive developments in digital infrastructure, connectivity in rural areas and innovation in farming businesses (e.g. Galloway et al., 2011; Kilpeläinen & Seppänen, 2014). The 2012 results of the annual Farm Business Survey suggest that computer usage and broadband adoption are at high levels amongst farmers (DEFRA, 2013). A detailed study by Oreszczyn et al. (2010) shows how UK farmers do not have a strong organisational framework but establish connections based on their shared identity in the form of distributed communities of practice. This characterisation of the farming profession suggests that networking tools like Twitter can be quite relevant since they facilitate quick information sharing based on weak ties. For example, offering opportunities to bring together different perspectives within the farming community and share experiences. An example of this is the Farming Selfie initiative that collects photos of farmers around the world with their animals ( As part of our assessment of the value of social media data, we collected and reviewed different accounts, common keywords and hashtags associated with UK farming on Twitter. Important insights to inform this selection came from a recent MSc dissertation on the topic by Simon Haley from Harper- Adams University (Haley, 2013). The aim was to explore how activity by farmers on Twitter could be potentially relevant in policy making processes and which tools and methodologies could support this input. We decided to focus our analysis on two main sources of data: 1. A dataset of seven discussions tagged with #AgriChatUK, which is a national Twitter conversation about farming that takes place weekly ( 2. An analysis of the content produced by followers of the main account of the National Farmers Union on Twitter (@NFUTweets) over a period of six was chosen as one of the major hubs that bring together farmers on Twitter further details about this choice are provided later in the report. Data collection and analysis was carried out from December 2014 to March It was supported by the following tools: Chorus to collect all the datasets used in the study, filter tweets based on keywords and produce keyword maps based on keyword frequency within tweets. Microsoft Excel to store extracted files, sort information and execute basic searches. 15

16 NVivo 10 to execute keyword frequency queries and visualise word clouds. An adaptation of the scripts proposed by Bruns (2012) to process tweets and extract mentions and retweets between users. The open graph visualisation platform Gephi ( to map and visualise networks based on mentions and retweets between users. The detailed steps and methodological choices made for each one of these are described in section 4 together with the findings Workshops and interviews with key informants In parallel to online data collection and analysis, the project explored how policy making teams understand the value of social media. Following an initial planning and kick- off meeting, the following activities took place: 1. A workshop with five participants to introduce the technical capabilities and visualisation features of social media analysis tools (December 2014). The workshop shaped initial ideas about the direction of the project. 2. Seven semi- structured interviews (January to March 2015) with policy makers that came from different levels of the civil service mainly related to communications and regulations about the environment, farming and local growth. Discussions evolved around the following questions: Generally, what type of input from external stakeholders does your role require? What kind of information flows support this input (e.g. consultations, surveys, other stakeholder engagement activities)? What are the different groups within the public that you would like to reach? How could information from social media change the ways in which you understand the needs of external stakeholders and the public? How do you think input from social media could support the work of your policy team and DEFRA in general? The main focus of these questions was to discover what the current information flows in public engagement are. A secondary objective was to directly inquire about the perceived potential of social media data. 3. A workshop with six participants to present and discuss the findings of the online mapping exercise (March 2015). This workshop shaped the project s conclusions and acted as the basis for the recommendations developed in this report. 16

17 4. Findings: farming and agricultural networks on Twitter Digital engagement offers important opportunities for farmers to network with their peers both for social and professional reasons (Haley, 2013). Communities of farmers on Twitter centre on regional/local networks around several hashtags that connect national conversations or information sharing; for example: #AgriChatUK (weekly national conversation about farming), #clubhectare (socialising around farming), #aggen (for young farmers to meet and connect), #BackBritishFarming (long term campaign organised by the National Farmers Union) and other more international or general hashtags (e.g. #AgriChat, #farming, #agriculture) #AgriChatUK: a national conversation about farming #AgriChatUK is the largest national farming discussion on Twitter that has been taking place every Thursday at 8-10pm since April The discussion is managed by a team of four volunteers and brings together farmers across the UK as well as important organisations involved in the industry 4. A different topic is chosen every week with questions posed at the beginning of the discussion by account. Discussions range in topics from farming history, rural broadband and food waste to more complicated issues that have direct regulatory interest, for example, the discussions on "Better regulation: livestock inspections" (September 2014) and England Common Agricultural Policy Consultation (November 2013) in was directly involved. Participants can comment on the questions and take part in dialogues that may not even include the original hashtag. Discussions have been archived on Storify via the AgriChatUK website 5. To examine the nature of #AgriChatUK discussions and profile of participants, we captured a total of 4,243 tweets tagged with #AgriChatUK that were posted between Wednesday 10th of December 2014 and Friday 20th of February This included seven different topics of discussion, two which were also related to #EnviroChatUK. The dataset included 2,033 retweets and 2,210 original tweets that were produced by 964 unique accounts. Close to 1,000 tweets contained links or linked material to a very wide range of resources including photos, videos, news articles, blog posts and reports related to the topics of discussion. 4 The following article on Farmers Weekly provides more information about #AgriChatUK and similar farming discussion groups: life/the- farmers- leading- a- twitter- revolution.htm 5 See for a history of summaries via Storify. 17

18 As expected, most tweets were posted (365 tweets), the account that coordinates the discussion. Other frequent contributors (200 (118 (102 tweets) (91 tweets). Frequent contributors broadly represent independent farmers as well as farming ambassadors and important organisations in the area. It is also important to observe that several contributors had large networks of followers at the time of the study (11K) and a few more with over 10K followers. This implies very good visibility of discussions taking place on #AgriChatUK beyond direct contributors. Networking analysis, as shown in figures 2 to 5 (visualised with the help of Gephi), suggests two important features of #AgriChatUK discussions. First, discussions seem centralised around a key group of core contributors that receive a lot of attention from the broader network in the form or mentions and retweets; this is indicated by the high number of incoming edges that the central nodes receive. Core contributors engage directly with each other (connected via edges). A filtering of figures 2 and 3 by the number of incoming edges (mentions/retweets or mentions respectively) shows 75 accounts mentioned or retweeted at least 10 times (figure 4) and 22 accounts mentioned at least 10 times excluding retweets (figure 5). As expected, this core group includes regular and frequent contributors to #AgriChatUK as well as account itself. Accounts in this group include important information hubs about farming, media accounts, farming entrepreneurs with large networks of followers and the personal accounts of the four volunteers that coordinate #AgriChatUK discussions. 18

19 Figure 2: #AgriChatUK network of mentions and retweets 3,038 edges Figure 3: #AgriChatUK network of mentions excluding retweets 2,019 edges Second, we can observe from figures 2 and 3 that the discussion is intensively conversational between users (overall density of the graph). Beyond the group of core contributors, there are many more users at the periphery of the network that interact with others using #AgriChatUK. The 4,243 tweets included 2,033 retweets (about 48%) without any modifications, 1,269 tweets (about 30%) that contain at least one mention or modified retweet and only 941 tweets (about 22%) that contain no mention to any other user (no inclusion of the ). The existence of a large number of users that make periodic contributions suggests that, through Twitter s network effects, discussions on #AgriChatUK are likely to reach a much larger number of users. Furthermore, the level of interaction suggests that #AgriChatUK operates in the form of a network of practice where a large number of participants have a broad interest around farming and may engage in weekly discussions depending on the topic. It is also reasonable to assume that many more Twitter users follow these discussions on a regular basis even though they only contribute occasionally or even not at all. Building on the analysis by Oreszczyn et al. (2010), #AgriChatUK is arguably one of the spaces that can bring together the regionally distributed networks of practice that traditionally exist in British farming. 19

20 Figure 4: #AgriChatUK network of 75 accounts mentioned or retweeted at least 10 times, about 25% of the full graph in figure 2 Figure 5: #AgriChatUK network of 22 accounts mentioned at least 10 times excluding retweets, about 12% of the full graph in figure 3 20

21 Who are these contributors to #AgriChatUK and to what extent do they reflect everyday farmers? How do they use Twitter outside their involvement in #AgriChatUK conversations? To further our investigation in this direction, we attempted to source more recent content (timeline data) from all users who had tweeted with the #AgriChatUK hashtag in our dataset. Starting from the original list of 964 contributors within our seven- weeks timeframe, we were able to quickly observe that these users are generally active and update their accounts frequently. For each user who had tweeted with the hashtag, we collected up to a maximum of their last 3,200 tweets 6 extending as far back into their Twitter history as that limit would allow. Figure 6: Timeline of 348 users over two months, each dot represents one day Given the high yield of data (up to 3,200 tweets from 964 unique accounts, amounting to a potential total of over three million tweets) and the exploratory nature of the project, we elected to manually and qualitatively filter the data to find users most relevant to the research. To begin the filtering process, we drew on users biographic metadata to remove accounts that tweeted particularly frequently (more likely to be automated Twitter bots or marketing accounts rather than users genuinely interested in agricultural issues) and accounts that seemed of less relevance to farming concerns. This left us with a selection of Twitter usernames for people who we could reasonably assume had a genuine and active interest in farming and agricultural issues, and talked about these issues online. Following this selection, we took a random sample of the remaining users timelines, collecting timeline data from 350 unique users back to January 1 st This refined dataset captured the entirety of selected users tweet output (to a maximum of their most recent 3,200 tweets) and comprised of 97,997 tweets covering a period from January 1 st to 6 The 3,200- tweet limit is a rule imposed by the Twitter Application Programming Interface (API), which is the mechanism by which Twitter allows users to retrieve data. 21

22 February 24 th A dataset of up to 100K tweets was preferable as its visualisation is more likely to give interpretable results. Findings from this new dataset indicated that these 350 users could be characterised as professionals since their tweeting volume was concentrated around Mondays to Fridays (see timeline in figure 6), or the typical working week. This is further verified by the prominence of URL- sharing throughout the dataset (denoted by the dark grey proportion of each interval in figure 6). Consistently, 40-50% of users tweets featured a URL, which suggests a concern with sharing information as opposed to merely delivering opinions. Sentiment analysis also showed that there are no significant trends professional users are generally less likely to post reactions containing sentiment either positive or negative (red trend line in figure 6). In terms of content, these users indeed seem to be talking mainly about farming. As shown in the cluster map in figure 7, agriculture was a distinct topic within this dataset and various agriculture- related terms appeared amongst the most frequent in the dataset farmers was the 6 th most frequent term and appeared in 2,834 tweets; milk the 13 th most frequent term appearing in 2,026 tweets, dairy the 15 th most frequent term appearing in 1,890 tweets and so on. The cluster map further shows that there are a few key terms around which several other sub- topics are scattered; these are mostly help, farmers, market, industry, support and British, which lie in a tightly packed central hub (labelled a. in figure 7). This central hub provides the underlying set of issues within which other more specific and contextualised farming talk is framed. For instance, from this central hub there are distinct sub- topic surrounding more specific aspects of agriculture such as: Milk pricing (labelled b.) Other dairy farming (labelled c.) Crops and soil management (labelled d.) Weather, climate change and clean energy (labelled e.) International food concerns (labelled f.) 7 Of the 350 selected users, 2 had not tweeted since January 1 st 2015 and as such were not captured in the dataset. 22

23 The way in which the wider conversation is structured suggests that most of the discussion around these sub- topics is themed by the wider frame of helping British farmers and supporting the British farming industry. These central issues inform several other sub- topic strands. This is overall good evidence about the existence of a network of practice within British farming that has shared concerns and aims about the profession. e. a. Figure 7: f. b. c. Cluster map of 97,997 tweets over two months from 350 contributors to #AgriChatUK d National Farmers Union (@NFUTweets) followers The analysis of #AgriChatUK conversations and further insights about its contributors has been a strong starting point in this research. To complement and compare findings from #AgriChatUK, we decided to follow a similar timeline analysis process for a collection of tweets coming from users who have only one thing in common: they follow one of the major Twitter hubs related to farming and agriculture. This analysis even more exploratory in nature could give us an indication of the formation of Twitter networks around farming from a wider perspective compared to a more stable community of farming entrepreneurs like #AgriChatUK. 23

24 The selection of a major hub for this analysis which is the main Twitter account used by the National Farmers Union to regularly post farming- related information to over 28K followers. The National Farmers Union has several other Twitter accounts to post more specific information around regions, tweets about national politics (@NFUPolitical) or updates about UK farming union in Brussels (@NFUsInBrussels). Another important reason for is that trade associations and unions have an institutional role in collective representation and a natural interest in promoting views from the profession along with their own objectives. Therefore, an analysis of their followers could reflect wider interest and networks around farming. At the time of the study, the following alternatives for influential accounts were also (13K followers): an account managed by a different farmer every week (created and coordinated by Simon (8K followers): Twitter account of the Farming Forum, which is the UK's largest agricultural and farming discussion (15K followers): the account that coordinates #AgriChatUK (45K followers): the Twitter account of Farmers Weekly magazine and (27K followers): the Twitter account of Farmers Guardian weekly national agricultural newspaper and website. Following this methodological choice, we extracted various metadata fields for the most recent 999 followers The decision to sample 999 followers was due to methodological and time constrains in any case, we assumed that this represents a good sample of followers. The metadata extracted was first used to plot an average tweets- per- day count as a normal distribution function (see figure 8) to determine which users tweeted abnormally frequently and identify users that tweet little. Working on the assumption that accounts which tweeted abnormally frequently were likely to be automated Twitter bots or marketing accounts, we retained in our dataset only those accounts whose average tweets- per- day count lay within one standard deviation. Further refining these data, we manually and qualitatively checked each remaining account to remove those that were less relevant to farming, leaving us with around 700 candidate accounts of interest. We captured six months of tweets (from 1st August 2014), yielding a dataset of 85,436 tweets this is a reasonably large dataset that still remains within manageable limits for an exploratory analysis. 24

25 Though these users were selected on the basis of their thereby having some form of interest in farming and agriculture, the nature of data collection had the potential to ensure a hugely varied set of topics from these users' timelines over a lengthy 6- month period. Visualising the dataset in Chorus' cluster explorer makes it clear (rightmost side of the overall term map in figure 9) that it is dominated by general talk such as you might expect to see in a random collection of everyday timelines. For instance, there is a topical strand around the terms "happy", "new" and "year", as well as general conversational terms such as "just", "thanks", "says", and so on Normal Dist Fn (y) 1 Standard Deviation 2 Standard Deviations 3 Standard Deviations Figure 8: Tweets- per- day count graph for the 999 followers It is interesting to observe that amongst this everyday conversation there are two separate farming- relevant branches that make up the leftmost side of the overall cluster map (labelled as 1 and 2 in figure 9). This demonstrates that tweeters are dividing their farming talk between dairy and arable farming. The collection of keywords used in each category serves to make a clear distinction between the two types of farming, with each category relying on a different lexicon in its expression. The first of these branches (label 1) concerns dairy farming and displays a messy array of multiple different themes and topics. Most notably, there are a selection of key terms towards the centre of the cluster: "farmer", "dairy", "cream", "milk", "british", and so on. These specific terms to dairy farming are situated closely to indirectly related topics such as fossil fuels, sustainability and climate change (with terms like "solar", "power", "clean", "energy", "green" and "party" for instance). Clearly for these users, dairy farming and renewable energy are related topics. This points to the value of social media data as a platform from which to qualitatively explore why such associations might exist. For example, why is renewable energy more of a topic of conversation specifically for dairy farmers? 25

26 2 Overall term map Figure 9: Cluster maps of 85,436 tweets over six months from 700 followers

27 The second key farming branch (label 2) concentrates on arable farming (i.e. terms like "land" and "food" as they appear alongside farming marketing terms like "shop" and "producers"), and shows a different type of conversation than that expressed around dairy. This is presented as a more linear conversation with fewer overall issues - this suggests either that arable land farming is less discussed on Twitter (by the selected users) than dairy farming, or that there are less sub- topics of arable farming that diversify the discussion. One particularly strongly- associated sub- topic is visible: terms like "economy", "government", "support", "local" and "community" demonstrate that the issue of government funding and local community are particularly interconnected with arable farming (as opposed to dairy farming for instance, which is surprising given media coverage on milk prices). This points to another potential investigation into farmers' perceptions of how government funding is directed, how local communities are perceived to feature in agricultural policy, and different impacts between arable and dairy farming. Figure 10 visualises a word cloud of 630 tweets that were filtered from this sub- topic. Figure 10. Word cloud of 630 tweets filtered There are also comparisons to be made from the cluster maps (figure 9) across the two branches - dairy and arable farming - in terms of their broader conversational patterns. For instance, dairy farming appears as a 'rhizomatic' and messy aggregation of a diverse array of issues - this is visible in the messy sprouting structure of the dairy- farming cluster. In contrast, arable farming appears as a more organised and perhaps less voluminous conversation, linearly presented with a few linear branches extending out from the central term "food". Exploring the differences between the tweeting practices around the topics of dairy and arable farming in this way may help to further characterise either conversation and help policy makers and researchers make better use of social media data. These distinct topics and the patterns of conversation can provide useful insight into everyday farming issues as discussed outside a more formal conversation such as #AgriChatUK. 27